






"""simple docstring"""


import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import BertGenerationDecoder, BertGenerationEncoder




class 						__UpperCamelCase     :
					def __init__(							self							:	str     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Dict=13     , UpperCAmelCase							:	List[str]=7     , UpperCAmelCase							:	Optional[int]=True     , UpperCAmelCase							:	Union[str, Any]=True     , UpperCAmelCase							:	List[str]=99     , UpperCAmelCase							:	int=32     , UpperCAmelCase							:	List[Any]=5     , UpperCAmelCase							:	List[Any]=4     , UpperCAmelCase							:	Union[str, Any]=37     , UpperCAmelCase							:	str="gelu"     , UpperCAmelCase							:	Tuple=0.1     , UpperCAmelCase							:	List[Any]=0.1     , UpperCAmelCase							:	Any=50     , UpperCAmelCase							:	Union[str, Any]=0.0_2     , UpperCAmelCase							:	Dict=True     , UpperCAmelCase							:	Optional[Any]=None     , )	->       Union[str, Any]:
											lowerCAmelCase   :Union[str, Any]						=			parent
											lowerCAmelCase   :List[str]						=			batch_size
											lowerCAmelCase   :List[str]						=			seq_length
											lowerCAmelCase   :List[Any]						=			is_training
											lowerCAmelCase   :Optional[Any]						=			use_input_mask
											lowerCAmelCase   :Tuple						=			vocab_size
											lowerCAmelCase   :List[Any]						=			hidden_size
											lowerCAmelCase   :Optional[int]						=			num_hidden_layers
											lowerCAmelCase   :List[str]						=			num_attention_heads
											lowerCAmelCase   :str						=			intermediate_size
											lowerCAmelCase   :Optional[Any]						=			hidden_act
											lowerCAmelCase   :str						=			hidden_dropout_prob
											lowerCAmelCase   :Dict						=			attention_probs_dropout_prob
											lowerCAmelCase   :Tuple						=			max_position_embeddings
											lowerCAmelCase   :List[str]						=			initializer_range
											lowerCAmelCase   :Dict						=			use_labels
											lowerCAmelCase   :List[Any]						=			scope
					def        UpperCAmelCase__ (							self							:	str   )	->       Any:
											lowerCAmelCase   :List[Any]						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											lowerCAmelCase   :Union[str, Any]						=			None
											if self.use_input_mask:
																	lowerCAmelCase   :List[str]						=			random_attention_mask([self.batch_size, self.seq_length]   )

											if self.use_labels:
																	lowerCAmelCase   :Any						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											lowerCAmelCase   :int						=			self.get_config()

											return config, input_ids, input_mask, token_labels
					def        UpperCAmelCase__ (							self							:	str   )	->       Any:
											return BertGenerationConfig(
											    vocab_size=self.vocab_size     , hidden_size=self.hidden_size     , num_hidden_layers=self.num_hidden_layers     , num_attention_heads=self.num_attention_heads     , intermediate_size=self.intermediate_size     , hidden_act=self.hidden_act     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , max_position_embeddings=self.max_position_embeddings     , is_decoder=UpperCAmelCase     , initializer_range=self.initializer_range     , )
					def        UpperCAmelCase__ (							self							:	int   )	->       int:
											(
											    (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, 
											)   :Optional[int]						=			self.prepare_config_and_inputs()

											lowerCAmelCase   :List[Any]						=			True
											lowerCAmelCase   :List[Any]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											lowerCAmelCase   :Optional[int]						=			ids_tensor([self.batch_size, self.seq_length]     , vocab_size=2   )

											return (
											    config,
											    input_ids,
											    input_mask,
											    token_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Dict     , **UpperCAmelCase							:	Optional[int]     , )	->       int:
											lowerCAmelCase   :Dict						=			BertGenerationEncoder(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :str						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase   )
											lowerCAmelCase   :Any						=			model(UpperCAmelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[int]     , **UpperCAmelCase							:	List[str]     , )	->       Dict:
											lowerCAmelCase   :List[str]						=			True
											lowerCAmelCase   :Tuple						=			BertGenerationEncoder(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :List[Any]						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , )
											lowerCAmelCase   :Tuple						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , )
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	int     , **UpperCAmelCase							:	Optional[int]     , )	->       Optional[int]:
											lowerCAmelCase   :List[Any]						=			True
											lowerCAmelCase   :Dict						=			True
											lowerCAmelCase   :str						=			BertGenerationDecoder(config=UpperCAmelCase   ).to(UpperCAmelCase   ).eval()

											# first forward pass
											lowerCAmelCase   :List[str]						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , use_cache=UpperCAmelCase     , )
											lowerCAmelCase   :List[str]						=			outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											lowerCAmelCase   :Any						=			ids_tensor((self.batch_size, 3)     , config.vocab_size   )
											lowerCAmelCase   :str						=			ids_tensor((self.batch_size, 3)     , vocab_size=2   )

											# append to next input_ids and
											lowerCAmelCase   :Union[str, Any]						=			torch.cat([input_ids, next_tokens]     , dim=-1   )
											lowerCAmelCase   :Optional[Any]						=			torch.cat([input_mask, next_mask]     , dim=-1   )

											lowerCAmelCase   :Tuple						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , output_hidden_states=UpperCAmelCase     , )['hidden_states'][0]
											lowerCAmelCase   :Tuple						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , past_key_values=UpperCAmelCase     , output_hidden_states=UpperCAmelCase     , )['hidden_states'][0]

											# select random slice
											lowerCAmelCase   :Optional[int]						=			ids_tensor((1,)     , output_from_past.shape[-1]   ).item()
											lowerCAmelCase   :Dict						=			output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase   :Optional[int]						=			output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-3   )   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Any     , *UpperCAmelCase							:	str     , )	->       Optional[int]:
											lowerCAmelCase   :Optional[int]						=			BertGenerationDecoder(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Optional[Any]						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , labels=UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Union[str, Any]:
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Tuple						=			self.prepare_config_and_inputs()
											lowerCAmelCase   :Any						=			{'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict




@require_torch
class 						__UpperCamelCase     (     UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				List[Any]      =  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
					lowercase_    :				Optional[int]      =  (BertGenerationDecoder,) if is_torch_available() else ()
					lowercase_    :				Any      =  (
					    {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
					    if is_torch_available()
					    else {}
					)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Tuple:
											lowerCAmelCase   :Optional[int]						=			BertGenerationEncoderTester(self   )
											lowerCAmelCase   :Tuple						=			ConfigTester(self     , config_class=UpperCAmelCase     , hidden_size=37   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											self.config_tester.run_common_tests()
					def        UpperCAmelCase__ (							self							:	str   )	->       Union[str, Any]:
											lowerCAmelCase   :Optional[int]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase   :str						=			'bert'
											self.model_tester.create_and_check_model(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Dict:
											lowerCAmelCase   :Dict						=			self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Optional[Any]:
											lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Union[str, Any]:
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, 
											)   :str						=			self.model_tester.prepare_config_and_inputs_for_decoder()

											lowerCAmelCase   :Dict						=			None

											self.model_tester.create_and_check_model_as_decoder(
											    UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											lowerCAmelCase   :Any						=			self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[Any]:
											lowerCAmelCase   :str						=			BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'   )
											self.assertIsNotNone(UpperCAmelCase   )




@require_torch
class 						__UpperCamelCase     (     unittest.TestCase						):
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'   )
											lowerCAmelCase   :Union[str, Any]						=			torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]   )
											with torch.no_grad():
																	lowerCAmelCase   :Tuple						=			model(UpperCAmelCase   )[0]
											lowerCAmelCase   :List[str]						=			torch.Size([1, 8, 1024]   )
											self.assertEqual(output.shape     , UpperCAmelCase   )
											lowerCAmelCase   :int						=			torch.tensor(
											    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]   )
											self.assertTrue(torch.allclose(output[:, :3, :3]     , UpperCAmelCase     , atol=1e-4   )   )




@require_torch
class 						__UpperCamelCase     (     unittest.TestCase						):
					@slow
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict:
											lowerCAmelCase   :int						=			BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'   )
											lowerCAmelCase   :Optional[int]						=			torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]   )
											with torch.no_grad():
																	lowerCAmelCase   :List[str]						=			model(UpperCAmelCase   )[0]
											lowerCAmelCase   :List[Any]						=			torch.Size([1, 8, 5_0358]   )
											self.assertEqual(output.shape     , UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			torch.tensor(
											    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]   )
											self.assertTrue(torch.allclose(output[:, :3, :3]     , UpperCAmelCase     , atol=1e-4   )   )







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2